"""
本地视觉模型加载器
支持多种模型格式(ONNX, TensorRT等)，实现模型推理和结果处理
"""

import os
import logging
import numpy as np
from typing import Optional, Dict, Any, Union, List
from pathlib import Path
import json
from abc import ABC, abstractmethod

try:
    import onnxruntime as ort
    ONNX_AVAILABLE = True
except ImportError:
    ONNX_AVAILABLE = False
    logging.warning("ONNX Runtime not available. ONNX models will not be supported.")

try:
    import torch
    import torchvision.transforms as transforms
    from transformers import AutoProcessor, AutoModelForVision2Seq, AutoTokenizer
    TORCH_AVAILABLE = True
except ImportError:
    TORCH_AVAILABLE = False
    logging.warning("PyTorch/Transformers not available. PyTorch models will not be supported.")

try:
    import tensorrt as trt
    import pycuda.driver as cuda
    import pycuda.autoinit
    TENSORRT_AVAILABLE = True
except ImportError:
    TENSORRT_AVAILABLE = False
    logging.warning("TensorRT not available. TensorRT models will not be supported.")


class ModelBackend(ABC):
    """模型后端抽象基类"""
    
    @abstractmethod
    def load_model(self, model_path: str) -> bool:
        """加载模型"""
        pass
    
    @abstractmethod
    def predict(self, image: np.ndarray, query: str) -> str:
        """执行推理"""
        pass
    
    @abstractmethod
    def is_available(self) -> bool:
        """检查后端是否可用"""
        pass


class ONNXBackend(ModelBackend):
    """ONNX模型后端"""
    
    def __init__(self):
        self.session = None
        self.input_name = None
        self.output_name = None
        
    def is_available(self) -> bool:
        return ONNX_AVAILABLE
    
    def load_model(self, model_path: str) -> bool:
        """加载ONNX模型"""
        if not self.is_available():
            return False
            
        try:
            # 配置ONNX Runtime会话选项
            sess_options = ort.SessionOptions()
            sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
            
            # 尝试使用GPU
            providers = ['CPUExecutionProvider']
            if ort.get_device() == 'GPU':
                providers.insert(0, 'CUDAExecutionProvider')
            
            self.session = ort.InferenceSession(model_path, sess_options, providers=providers)
            
            # 获取输入输出名称
            self.input_name = self.session.get_inputs()[0].name
            self.output_name = self.session.get_outputs()[0].name
            
            logging.info(f"Successfully loaded ONNX model from {model_path}")
            return True
            
        except Exception as e:
            logging.error(f"Failed to load ONNX model: {e}")
            return False
    
    def predict(self, image: np.ndarray, query: str) -> str:
        """ONNX模型推理"""
        if self.session is None:
            return "Model not loaded"
        
        try:
            # 预处理图像
            processed_image = self._preprocess_image(image)
            
            # 执行推理
            result = self.session.run([self.output_name], {self.input_name: processed_image})
            
            # 后处理结果
            return self._postprocess_result(result[0], query)
            
        except Exception as e:
            logging.error(f"ONNX inference failed: {e}")
            return f"Inference error: {str(e)}"
    
    def _preprocess_image(self, image: np.ndarray) -> np.ndarray:
        """预处理图像"""
        # 标准化图像预处理
        if len(image.shape) == 3:
            image = np.transpose(image, (2, 0, 1))  # HWC to CHW
        
        # 归一化到[0,1]
        image = image.astype(np.float32) / 255.0
        
        # 添加batch维度
        image = np.expand_dims(image, axis=0)
        
        return image
    
    def _postprocess_result(self, result: np.ndarray, query: str) -> str:
        """后处理推理结果"""
        # 简单的结果处理，实际实现需要根据具体模型调整
        if len(result.shape) > 1:
            result = result.flatten()
        
        # 这里应该根据具体模型的输出格式进行解码
        # 暂时返回一个基本的描述
        return f"Image analysis result for query '{query}': {result[:5].tolist()}"


class TorchBackend(ModelBackend):
    """PyTorch/Transformers模型后端"""
    
    def __init__(self):
        self.model = None
        self.processor = None
        self.tokenizer = None
        self.device = None
        
    def is_available(self) -> bool:
        return TORCH_AVAILABLE
    
    def load_model(self, model_path: str) -> bool:
        """加载PyTorch模型"""
        if not self.is_available():
            return False
            
        try:
            # 设置设备
            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            
            # 加载模型和处理器
            if os.path.isdir(model_path):
                # 从目录加载Hugging Face模型
                self.model = AutoModelForVision2Seq.from_pretrained(model_path)
                self.processor = AutoProcessor.from_pretrained(model_path)
                self.tokenizer = AutoTokenizer.from_pretrained(model_path)
            else:
                # 从文件加载PyTorch模型
                self.model = torch.load(model_path, map_location=self.device)
            
            self.model.to(self.device)
            self.model.eval()
            
            logging.info(f"Successfully loaded PyTorch model from {model_path}")
            return True
            
        except Exception as e:
            logging.error(f"Failed to load PyTorch model: {e}")
            return False
    
    def predict(self, image: np.ndarray, query: str) -> str:
        """PyTorch模型推理"""
        if self.model is None:
            return "Model not loaded"
        
        try:
            with torch.no_grad():
                if self.processor is not None:
                    # 使用Hugging Face处理器
                    inputs = self.processor(images=image, text=query, return_tensors="pt")
                    inputs = {k: v.to(self.device) for k, v in inputs.items()}
                    
                    outputs = self.model.generate(**inputs, max_length=100)
                    result = self.processor.decode(outputs[0], skip_special_tokens=True)
                else:
                    # 基本的PyTorch推理
                    image_tensor = self._preprocess_image_torch(image)
                    outputs = self.model(image_tensor)
                    result = self._postprocess_torch_result(outputs, query)
                
                return result
                
        except Exception as e:
            logging.error(f"PyTorch inference failed: {e}")
            return f"Inference error: {str(e)}"
    
    def _preprocess_image_torch(self, image: np.ndarray):
        """PyTorch图像预处理"""
        transform = transforms.Compose([
            transforms.ToPILImage(),
            transforms.Resize((224, 224)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        ])
        
        image_tensor = transform(image).unsqueeze(0).to(self.device)
        return image_tensor
    
    def _postprocess_torch_result(self, outputs, query: str) -> str:
        """PyTorch结果后处理"""
        # 简单的结果处理
        if hasattr(outputs, 'logits'):
            outputs = outputs.logits
        
        # 获取预测结果
        predictions = torch.softmax(outputs, dim=-1)
        top_pred = torch.argmax(predictions, dim=-1)
        
        return f"Image analysis for '{query}': prediction {top_pred.item()}"


class TensorRTBackend(ModelBackend):
    """TensorRT模型后端"""
    
    def __init__(self):
        self.engine = None
        self.context = None
        self.inputs = []
        self.outputs = []
        self.bindings = []
        self.stream = None
        
    def is_available(self) -> bool:
        return TENSORRT_AVAILABLE
    
    def load_model(self, model_path: str) -> bool:
        """加载TensorRT引擎"""
        if not self.is_available():
            return False
            
        try:
            # 加载TensorRT引擎
            with open(model_path, 'rb') as f:
                runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING))
                self.engine = runtime.deserialize_cuda_engine(f.read())
            
            self.context = self.engine.create_execution_context()
            self.stream = cuda.Stream()
            
            # 分配GPU内存
            for binding in self.engine:
                size = trt.volume(self.engine.get_binding_shape(binding)) * self.engine.max_batch_size
                dtype = trt.nptype(self.engine.get_binding_dtype(binding))
                
                # 分配主机和设备内存
                host_mem = cuda.pagelocked_empty(size, dtype)
                device_mem = cuda.mem_alloc(host_mem.nbytes)
                
                self.bindings.append(int(device_mem))
                
                if self.engine.binding_is_input(binding):
                    self.inputs.append({'host': host_mem, 'device': device_mem})
                else:
                    self.outputs.append({'host': host_mem, 'device': device_mem})
            
            logging.info(f"Successfully loaded TensorRT engine from {model_path}")
            return True
            
        except Exception as e:
            logging.error(f"Failed to load TensorRT engine: {e}")
            return False
    
    def predict(self, image: np.ndarray, query: str) -> str:
        """TensorRT推理"""
        if self.engine is None:
            return "Engine not loaded"
        
        try:
            # 预处理图像
            processed_image = self._preprocess_image_trt(image)
            
            # 复制输入数据到GPU
            np.copyto(self.inputs[0]['host'], processed_image.ravel())
            cuda.memcpy_htod_async(self.inputs[0]['device'], self.inputs[0]['host'], self.stream)
            
            # 执行推理
            self.context.execute_async_v2(bindings=self.bindings, stream_handle=self.stream.handle)
            
            # 复制输出数据到CPU
            cuda.memcpy_dtoh_async(self.outputs[0]['host'], self.outputs[0]['device'], self.stream)
            self.stream.synchronize()
            
            # 后处理结果
            result = self._postprocess_trt_result(self.outputs[0]['host'], query)
            return result
            
        except Exception as e:
            logging.error(f"TensorRT inference failed: {e}")
            return f"Inference error: {str(e)}"
    
    def _preprocess_image_trt(self, image: np.ndarray) -> np.ndarray:
        """TensorRT图像预处理"""
        # 标准化预处理
        image = image.astype(np.float32) / 255.0
        if len(image.shape) == 3:
            image = np.transpose(image, (2, 0, 1))
        return image
    
    def _postprocess_trt_result(self, result: np.ndarray, query: str) -> str:
        """TensorRT结果后处理"""
        return f"TensorRT analysis for '{query}': {result[:5].tolist()}"


class LocalVisionModel:
    """
    本地视觉模型加载器
    支持多种模型格式: ONNX, TensorRT, PyTorch等
    """
    
    def __init__(self, model_path: str, model_type: Optional[str] = None):
        """
        初始化本地视觉模型
        
        Args:
            model_path: 模型文件路径
            model_type: 模型类型 ('onnx', 'pytorch', 'tensorrt')，如果为None则自动检测
        """
        self.model_path = Path(model_path)
        self.model_type = model_type or self._detect_model_type()
        self.backend = None
        self.is_loaded = False
        
        # 初始化日志
        self.logger = logging.getLogger(__name__)
        
        # 加载模型
        self._initialize_backend()
    
    def _detect_model_type(self) -> str:
        """自动检测模型类型"""
        if not self.model_path.exists():
            raise FileNotFoundError(f"Model file not found: {self.model_path}")
        
        suffix = self.model_path.suffix.lower()
        
        if suffix == '.onnx':
            return 'onnx'
        elif suffix in ['.pt', '.pth']:
            return 'pytorch'
        elif suffix in ['.trt', '.engine']:
            return 'tensorrt'
        elif self.model_path.is_dir():
            # 检查是否为Hugging Face模型目录
            if (self.model_path / 'config.json').exists():
                return 'pytorch'
        
        # 默认尝试ONNX
        return 'onnx'
    
    def _initialize_backend(self):
        """初始化模型后端"""
        backends = {
            'onnx': ONNXBackend,
            'pytorch': TorchBackend,
            'tensorrt': TensorRTBackend
        }
        
        if self.model_type not in backends:
            raise ValueError(f"Unsupported model type: {self.model_type}")
        
        backend_class = backends[self.model_type]
        self.backend = backend_class()
        
        if not self.backend.is_available():
            raise RuntimeError(f"{self.model_type} backend is not available")
        
        # 加载模型
        self.is_loaded = self.backend.load_model(str(self.model_path))
        
        if not self.is_loaded:
            raise RuntimeError(f"Failed to load model: {self.model_path}")
    
    def analyze_image(self, image: np.ndarray, query: str = "Describe this image") -> str:
        """
        分析图像并返回描述
        
        Args:
            image: 输入图像 (numpy数组)
            query: 查询文本
            
        Returns:
            图像分析结果
        """
        if not self.is_loaded:
            return "Model not loaded"
        
        if image is None or image.size == 0:
            return "Invalid image input"
        
        try:
            result = self.backend.predict(image, query)
            self.logger.info(f"Image analysis completed for query: {query}")
            return result
            
        except Exception as e:
            self.logger.error(f"Image analysis failed: {e}")
            return f"Analysis failed: {str(e)}"
    
    def get_model_info(self) -> Dict[str, Any]:
        """获取模型信息"""
        return {
            'model_path': str(self.model_path),
            'model_type': self.model_type,
            'is_loaded': self.is_loaded,
            'backend_available': self.backend.is_available() if self.backend else False
        }
    
    def is_model_loaded(self) -> bool:
        """检查模型是否已加载"""
        return self.is_loaded
    
    @staticmethod
    def get_supported_formats() -> List[str]:
        """获取支持的模型格式"""
        formats = []
        
        if ONNX_AVAILABLE:
            formats.append('onnx')
        if TORCH_AVAILABLE:
            formats.append('pytorch')
        if TENSORRT_AVAILABLE:
            formats.append('tensorrt')
        
        return formats
    
    @staticmethod
    def check_dependencies() -> Dict[str, bool]:
        """检查依赖项可用性"""
        return {
            'onnxruntime': ONNX_AVAILABLE,
            'pytorch': TORCH_AVAILABLE,
            'tensorrt': TENSORRT_AVAILABLE
        }


def create_local_vision_model(model_path: str, model_type: Optional[str] = None) -> LocalVisionModel:
    """
    工厂函数：创建本地视觉模型实例
    
    Args:
        model_path: 模型文件路径
        model_type: 模型类型
        
    Returns:
        LocalVisionModel实例
    """
    return LocalVisionModel(model_path, model_type)


# 示例使用
if __name__ == "__main__":
    # 配置日志
    logging.basicConfig(level=logging.INFO)
    
    # 检查依赖项
    deps = LocalVisionModel.check_dependencies()
    print("Available backends:", deps)
    
    # 获取支持的格式
    formats = LocalVisionModel.get_supported_formats()
    print("Supported formats:", formats)
    
    # 示例：创建模型实例（需要实际的模型文件）
    # model = create_local_vision_model("/path/to/model.onnx")
    # result = model.analyze_image(image_array, "What do you see in this image?")
    # print(result)